{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import joblib\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_01 = joblib.load('../../3.HA_model/1.PCA_T2/train_HA_feature/train_01.lz4')\n",
    "train_02 = joblib.load('../../3.HA_model/1.PCA_T2/train_HA_feature/train_02.lz4')\n",
    "train_03 = joblib.load('../../3.HA_model/1.PCA_T2/train_HA_feature/train_03.lz4')\n",
    "\n",
    "test_01 = joblib.load('../../3.HA_model/1.PCA_T2/test_HA_feature/test_01.lz4')\n",
    "test_02 = joblib.load('../../3.HA_model/1.PCA_T2/test_HA_feature/test_02.lz4')\n",
    "test_03 = joblib.load('../../3.HA_model/1.PCA_T2/test_HA_feature/test_03.lz4')\n",
    "test_04 = joblib.load('../../3.HA_model/1.PCA_T2/test_HA_feature/test_04.lz4')\n",
    "test_05 = joblib.load('../../3.HA_model/1.PCA_T2/test_HA_feature/test_05.lz4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练数据经过剪切了\n",
    "train = pd.concat([train_01, train_02, train_03], axis=0)\n",
    "y_train = train[['RULR']]\n",
    "use_cols = ['PCA_T2__mean', 'PCA_T2__mean_diff', 'PCA_T2__delta', 'PCA_T2__delta_diff']\n",
    "x_train = train[use_cols]\n",
    "\n",
    "test = pd.concat([test_01, test_02, test_03, test_04, test_05], axis=0)\n",
    "use_cols = ['PCA_T2__mean', 'PCA_T2__mean_diff', 'PCA_T2__delta', 'PCA_T2__delta_diff']\n",
    "x_test = test[use_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, maxdepth=6, min_child_weight=1, missing=None,\n",
       "       n_estimators=300, n_jobs=8, nthread=None, objective='reg:linear',\n",
       "       random_state=0, random_state =2018, reg_alpha=0, reg_lambda=5,\n",
       "       scale_pos_weight=1, seed=None, silent=True, subsample=1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost.sklearn import XGBRegressor\n",
    "params = {'maxdepth': 6, 'learning_rate': 0.1, 'n_estimators': 300, 'objective': 'reg:linear',\n",
    "         'booster': 'gbtree', 'n_jobs': 8, 'reg_lambda': 5, 'random_state ':2018}\n",
    "\n",
    "xgb = XGBRegressor(**params)\n",
    "xgb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.avg\n",
    "RULR = xgb.predict(x_test).reshape(-1,)\n",
    "CLR = 1 - RULR\n",
    "CL = test['CL'].values\n",
    "# [0.335, 0.5125, 0.2585, 0.5105, 0.6905] 为5个test文件的时间片段内的真实剩余寿命（RULR）取平均值\n",
    "# 以下代码用于计算预测的平均剩余寿命与真实平均剩余寿命的mae值\n",
    "# residual = avg - np.array([0.335, 0.5125, 0.2585, 0.5105, 0.6905])\n",
    "# np.abs(residual).sum()/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff6dbc48ac8>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.subplot(3,2,1)\n",
    "plt.plot(CL[:8],CLR[:8])\n",
    "\n",
    "plt.subplot(3,2,2)\n",
    "plt.plot(CL[8:15],CLR[8:15])\n",
    "\n",
    "plt.subplot(3,2,3)\n",
    "plt.plot(CL[15:25],CLR[15:25])\n",
    "\n",
    "plt.subplot(3,2,4)\n",
    "plt.plot(CL[25:35],CLR[25:35])\n",
    "\n",
    "plt.subplot(3,2,5)\n",
    "plt.plot(CL[35:45],CLR[35:45])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
